WpyQwq
643e22ecb9
- 引入 MemoryRouterXL 与 v5/v6 流式多线程训练/编码管线 - 修复 prepare_memory_router_dataset 候选池重建缺陷（mega 家族 3568x 加速，输出逐字节相同） - 修复 v5 被破坏的拒答与多跳标签（train 未知样本 319 -> 16319，multi_hop 平均正例 1.00 -> 2.00） - 同存储预算下 V2-128 v6 逐轴 22/22 通过：Top-1 41.12% -> 94.62%，未知拒答 0.00% -> 100.00% - 记录三条被实测推翻的显然优化（logits_to_keep=1 反而慢 55%、XL 容量未带来收益） - 记忆手术跨架构可移植性 14/14，读写关闭时与原生模型逐位相同
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
"""Evaluate a saved dynamic-memory checkpoint."""
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from __future__ import annotations
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import argparse
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import torch
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from .model import DynamicMemoryConfig, DynamicMemoryLM
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from .tasks import sample_associative_batch
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--checkpoint", default="V2_dpskw/checkpoints/latest.pt")
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parser.add_argument("--batches", type=int, default=100)
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parser.add_argument("--device", default="auto", choices=("auto", "cpu", "cuda"))
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args = parser.parse_args()
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device = torch.device("cuda" if args.device == "auto" and torch.cuda.is_available() else ("cpu" if args.device == "auto" else args.device))
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checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False)
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config = DynamicMemoryConfig(**checkpoint["config"])
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model = DynamicMemoryLM(config).to(device)
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model.load_state_dict(checkpoint["model"])
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model.eval()
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for overwrite in (False, True):
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correct = 0
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total = 0
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with torch.no_grad():
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for _ in range(args.batches):
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batch = sample_associative_batch(
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batch_size=256,
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vocab_size=config.vocab_size,
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device=device,
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overwrite=overwrite,
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)
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memory = None
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for chunk in batch.learn_chunks:
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memory = model(chunk, memory=memory, update_memory=True).memory
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output = model(batch.query_input, memory=memory, update_memory=False)
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prediction = output.logits[:, 0].argmax(dim=-1)
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correct += int((prediction == batch.expected).sum())
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total += batch.expected.numel()
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print(f"overwrite={overwrite} accuracy={correct / total:.3f}")
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if __name__ == "__main__":
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main()
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